1. Field of the Invention
Embodiments relate to data compression of a signal.
2. Brief Summary of the Invention
Embodiments provide a method for vector quantization of a feature vector, in particular with respect to data compression of a signal to be transmitted or stored, particularly a voice signal or video signal, wherein at least one codebook consisting of multiple codebook vectors is searched to find a code vector representing the feature vector, and during the search a sequence of codebook vectors is tested to determine their suitability to represent the feature vector.
This type of method is suitable for representing, i.e., in particular for coding, multi-dimensional feature vectors whose vector components are derived from an often wide range of possible values, using a comparatively small number of codebook vectors or using the indices for these codebook vectors, which clearly identify these codebook vectors. Such methods are used in connection with many common coding processes, especially in the area of voice, image, or video coding, but also for coding other signals, especially for the purpose of reducing the volume of the data to be transmitted or stored (data compression). The data volume is reduced by, among other things, replacing a number of different feature vectors with the same code vector, which represents all of these different feature vectors. A coding error occurs in such a case, because of the differences between these feature vectors, and the greater the distance between the feature vector to be coded and the code vector representing it, the greater this coding error becomes. The distance between vectors is measured here using a distance measurement defined by the application, which should have the mathematical characteristics of a metric or a standard.
Each codebook vector of a codebook is associated with a cell that surrounds this codebook vector in the feature vector area, which contains all those feature vectors that are represented by the codebook vector of this cell. This cell corresponds to the volume of all codebook vectors which, based on the selected distance measurement, do not lie closer to any other codebook vector of the codebook than to the codebook vector that is surrounded by this cell. The shape of the cell therefore depends upon the selected distance measurement and on the distribution of the codebook vectors in the feature vector area.
The greater the number of codebook vectors and the greater the density at which these codebook vectors are placed in the feature vector area, the lower the information loss related to such methods. In such cases, codebook vectors must not be distributed symmetrically in the feature vector area, but it can be advisable to have a concentration of codebook vectors in those parts of the feature vector area where representation precision is especially necessary or desirable. However, as the number of codebook vectors and their density in the feature vector area increases, so does the cost of determining an optimal code vector or even just a close-to-optimal code vector that best (or with acceptable errors) represents the feature vector to be coded.
Embodiments may offer a method for vector quantization of a feature vector which (compared to currently known methods) combines the greatest possible (increase in) precision with the lowest possible (increase in) cost.